An Inventory Optimization Model for Complex Systems with Imprecise Demand and Variable Holding Cost using AI Forecasting

Vinay Kumar Masiyare; Animesh Kumar Sharma

doi:10.52228/JRUB.2026-39-1-11

Journal of Ravishankar University

Pt. Ravishankar Shukla University, Raipur, Chhattisgarh

PART-B

(SCIENCE)

ISSN: 0970-5910

Abstract View

Submit Article

An Inventory Optimization Model for Complex Systems with Imprecise Demand and Variable Holding Cost using AI Forecasting

Author(s): Vinay Kumar Masiyare, Animesh Kumar Sharma

Email(s): vinaym.phd2024@iuraipur.edu.in

Address: Research Scholar, Department of Mathematics, The ICFAI University, Raipur, Chhattisgarh, India
Assistant Professor, Department of Mathematics, The ICFAI University, Raipur, Chhattisgarh, India

*Corresponding Author’s Email: vinaym.phd2024@iuraipur.edu.in

Published In: Volume - 39, Issue - 1, Year - 2026

DOI: 10.52228/JRUB.2026-39-1-11

View HTML

View PDF

ABSTRACT:
Handling inventory in complex systems needs holistic approach that can significantly solve the uncertainties, the deterioration and fluctuation in the holding costs. Conventional models of EOQ are based on deterministic assumptions of a fixed holding cost and demand which is always constant hence not suitable in real life situations. In order to avoid these shortcomings, this paper introduces an integrated inventory model that integrates AI assisted demand forecasting, fuzzy logic based uncertainty modelling, and deterioration related holding cost structure into a single optimization framework. The analysis of historical demand data is performed with the help of Long Short-Term Memory (LSTM) neural network, which displays nonlinear changes that time may bring and provides a highly precise forecast of 110.68 units in the next cycle. As AI predictions are fundamentally uncertain, an asymmetric triangular fuzzy number is used to reflect pessimistic, most likely, and optimistic demand cases. The fuzzy representation is subsequently defuzzified giving rise to an effective demand of 111.60 units which becomes a strong and modified input to the optimization model. The deterioration associated with holding cost has been included in the proposed model in order to ensure that the rise in storage costs attributed to perishable products is reflected realistically. The objective of the optimization is the minimization of the Average Total Cost (ATC) by establishing the optimal order quantity and cycle time. The numerical example demonstrates that the best cycle time is about 0.65, order quantity is about 72.24 units, and the lowest cost is 152.80 per cycle time. Sensitivity analysis shows that the system behaviour remains stable with changes in the demand, holding cost, deterioration cost and deterioration rate. In general, the combined method based on AI, Fuzzy and Deterioration presents a realistic, strong, and managerial applicable framework of the optimal use of inventory decisions in uncertain and dynamic conditions.

Keywords:

Cite this article:
Masiyare and Sharma (2026). An Inventory Optimization Model for Complex Systems with Imprecise Demand and Variable Holding Cost using AI Forecasting. Journal of Ravishankar University (Part-B: Science), 39(1), pp. 186-200. DOI:https://doi.org/10.52228/JRUB.2026-39-1-11

References

1. Karimi, M. (2025). Inventory control of deteriorating items: A review. International Journal of Industrial Engineering Computations, 16, 93–128.

2. Verma, R., Narang, P, & De, PK. (2025). An EOQ Model with Time-Dependent Demand and Holding Cost Under the Effect of Inspection on Deterioration Rate Journal of Industrial Integration and Management, 9(4), 597-625

3. Tiwari, A., Pillai, J., & Janghel, R. R. (2025). Leveraging LSTM for precision inventory management by future demand forecasting. Journal of applied research and technology, 23(1), 8-21.

4. Singh, T., Mishra, P. J., & Pattanayak, H. (2018). An EOQ inventory model for deteriorating items with time-dependent deterioration rate, ramp-type demand rate and shortages. International Journal of Mathematics in Operational Research, 12(4), 423-437.

5. Santhi, G., & Karthikeyan, K. (2021). Two-warehouse inventory model for instantaneous deteriorating items with price and time dependent demand, complete backlogging. Italian Journal of Pure and Applied Mathematics, 46, 24-33.

6. Vahdani, M., Sazvar, Z., & Govindan, K. (2022). An integrated economic disposal and lot-sizing problem for perishable inventories with batch production and corrupt stock-dependent holding cost. Annals of Operations Research, 315(2), 2135-2167.

7. Soni, H. N., & Suthar, S. N. (2021). EOQ model of deteriorating items for fuzzy demand and learning in fuzziness with finite horizon. Journal of Control and Decision, 8(2), 89-97.

8. Das, S. K. (2022). A fuzzy multi objective inventory model of demand dependent deterioration including lead time. Journal of fuzzy extension and applications, 3(1), 1-18.

9. Tiwari, S., Shah, K., & Bhimani, K. (2024). EPQ model with the effect of inflation and reliability for partial trade credit under fuzzy and cloudy fuzzy environment. Journal of Management Analytics, 11(1), 110-134.

10. Su, Y., Wang, M. C., & Liu, S. (2024). Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting. Computers, Materials & Continua, 78(3).

11. Das, A. T. V. R., Kumar, K. R., Satyanarayana, M., & Nethala, T. (2024). Time Series Forecasting and Modelling of Food Demand Supply Chain Based on Regressors Analysis. Cahiers Magellanes-Ns, 6(2), 2885-2893.

12. Kumar, V., & Goyal, S. (2025). AI-Driven Forecasting and Optimization for Inventory Control in Manufacturing Supply Chain. Advances in Consumer Research, 2, 5085-5091.

Related Images: